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rashmita uppalli
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North Carolina, United States
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Gen AI / ML EngineerFirst Citizens BankMay 2025 - Current (1 year 4 months)Project : Enterprise Agentic Wealth & Commercial Banking Intelligence Platform • Architected a Production-Grade Hybrid RAG Pipeline: Developed an enterprise financial intelligence ecosystem using GPT-4 and Weaviate. Integrated Hybrid Search (Vector + BM25) to ensure 99.2% retrieval recall across massive corporate portfolios, commercial loan agreements, and complex regulatory terminologies. • Built advanced RAG pipelines using LangChain retrievers, embedding models, and Weaviate vector databases, enabling context-aware retrieval across structured and unstructured financial knowledge sources. • Multi-Agent Orchestration & Automation: Engineered a LangGraph-based multi-agent framework comprising specialized agents (Financial Planner, Portfolio- CAI / ML & Generative AI EngineerCarrier GlobalApr 2024 - Mar 2025 (1 year)Project: Multi-Agent Intelligent Building Operations & Predictive Maintenance Ecosystem • Agentic Maintenance Orchestration: Architected an enterprise-grade multi-agent system using LangGraph and Amazon Bedrock (Claude 3.5 Sonnet) to automate commercial HVAC anomaly triage. Engineered a specialized topology of agents—including a Telemetry Analyst Agent, Diagnostic Agent, and Safety Coordinator Agent—reducing building downtime by 35%. • Leveraged LangChain and LangGraph frameworks to coordinate multi-agent workflows, enabling dynamic task planning, tool integration, and automated decision-making across HVAC diagnostic processes. • GraphRAG Knowledge Fusion: Engineered a hybrid retrieval ecosystem combining vector search (Weaviate) and a Know
- ZAI / ML EngineerZoron IT Solution, IndiaAug 2021 - Jan 2024 (2 years 6 months)• Predictive Intelligence: Developed and productionized high-impact Gradient Boosted models (XGBoost, LightGBM) for HR Attrition, achieving 85% accuracy through custom feature engineering and Bayesian hyperparameter optimization. • MLOps & Lifecycle Architecture: Standardized the machine learning lifecycle by implementing MLflow for experiment tracking and Prefect for automated DAG orchestration, reducing model time-to-market by 30% through rigorous data-driven experimentation. • Advanced NLP & Analytics: Leveraged BERT-based embeddings and Cosine Similarity for semantic text classification, improving the accuracy of client feedback categorization by 22% and building visualization layers to interpret model outputs for key business stakehold
Education
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- MMaster of Science in Information SystemsJan 2024 - May 2025 (1 year 5 months)